Cross-Domain Recommendation (CDR) is an effective way to alleviate the cold-start problem. However, previous work severely ignores fairness and bias when learning the mapping function, which is used to obtain the representations for fresh users in the target domain. To study this problem, in this paper, we propose a Fairness-aware Cross-Domain Recommendation model, called FairCDR. Our method achieves user-oriented group fairness by learning the fairness-aware mapping function. Since the overlapping data are quite limited and distributionally biased, FairCDR leverages abundant non-overlapping users and interactions to help alleviate these problems. Considering that each individual has different influence on model fairness, we propose a new reweighing method based on Influence Function (IF) to reduce unfairness while maintaining recommendation accuracy. Extensive experiments are conducted to demonstrate the effectiveness of our model.

翻译：跨域推荐（CDR）是缓解冷启动问题的有效方法。然而，以往研究在学习用于获取目标领域新用户表示的映射函数时严重忽视了公平性与偏差问题。针对这一问题，本文提出了一种公平感知跨域推荐模型，命名为FairCDR。该方法通过学习公平感知映射函数实现了面向用户的群体公平性。由于重叠数据极为有限且存在分布偏差，FairCDR充分利用大量非重叠用户及交互数据来缓解这些问题。考虑到每个个体对模型公平性具有不同影响，我们基于影响函数（IF）提出了一种新的重加权方法，在保持推荐精度的同时降低不公平性。大量实验验证了该模型的有效性。